1. Field of the Invention
The presently disclosed invention relates to an apparatus and method for collecting satellite images of bodies of water such as the oceans and removing interfering clouds from the satellite images to improve the accuracy of the images. More particularly, the presently disclosed invention relates to the use of an apparatus and method to extract cloud edge information through local segmentation of the image and a procedure to discriminate between cloud free and cloud contaminated pixels. The cloud contaminated pixels are then removed from daytime and nighttime 1-km advanced very high resolution radiometer (AVHRR) satellite image.
1. Description of the Related Art
The quality of sea surface temperature (SST) measurements derived from spaceborn instruments depends largely on the accuracy of the cloud removal technique to eliminate cloud-contaminated pixels from the analysis. Clouds present a complicated problem. Generally, clouds are characterized by high albedos (i.e. the proportion of light or radiation reflected by a surface, especially of a planet or moon, for example, the whiteness of a reflection, The Concise Oxford Dictionary of Current English, Eighth Edition, R. E. Allen Ed., Clarendon Press, Oxford (1990)). In the daytime data collected, clouds have a greater whiteness or albedo than the surrounding non-cloud image. Clouds are also characterized by lower temperatures than the surrounding image. When clouds do not fill the instantaneous field of view (IFOV) of the instrument sensor, the cloud signatures become difficult to separate from the surrounding targets. Cirrus, low stratus and small cumulus are examples of clouds whose signatures can introduce large errors to the SST measurements.
Methods of cloud removal from satellite images vary from simple radiance thresholds to rather complicated procedures involving artificial intelligence techniques. Because a single technique does not appear to be sufficient in eliminating time and geographic dependency inherent within most techniques, the tendency is to combine a series of techniques. For instance, the operational cloud masking routine developed for the National Environmental Satellite Data and Information Service of the National Oceanic and Atmospheric Administration (NOAA NESDIS) uses a series of tests (i.e. gross cloud test, visible, thermal IR tests, and low stratus test) designed to eliminate different cloud signatures at several stages. The techniques implemented by the SeaSpace Corporation, San Diego, Calif. (SEA) remove clouds from AVHRR scenes based on a series of tests and thresholds similar to (but not the same as) those used by NOAA NESDIS. These latter procedures, which allow for further relaxation of the input parameter, produce less conservative (i.e. more cloud contaminated pixels are present in the satellite image) images than the NOAA NESDIS techniques. Simpson and Humphrey, infra methods rely on a combination of visible and infrared data, empirical albedo model as function of solar zenith angle, a radiance threshold, and sampling techniques across the image to insure that all Sun-pixel-satellite angles associated with that image are included in the calculations. See Simpson, J. J. and Humphrey, C., An automated cloud screening algorithm for daytime advanced very high resolution radiometer imagery, J. Geophys. Res., 95, 13, 459-13, 481 (1990), incorporated by reference herein in its entirety for all purposes. Although the methods may vary, standard cloud techniques rely on spectral rather than on texture signatures of the scene.
Ebert demonstrated that the introduction of pattern recognition techniques to AVHRR cloud removal methods produced cloud cover and cloud field structure information with great accuracy and less computation time than techniques that used spectral information exclusively. See Ebert, E., A pattern recognition technique for distinguishing surface and cloud types in the polar regions, J. Clin. Appl. Meteorol., 26, 1412-1427 (1987), incorporated by reference herein in its entirety for all purposes. Texture information alone extracted from a single visible channel was sufficient to detect, segment and classify different cloud types in polar regions. The gray level co-occurrence (GLC) matrix is one approach for extracting texture information of a scene based on the overall spatial relationship existing among its gray levels (i.e. intensity). See Haralick, R. M., Shanmugam, K., and Denstein, I., Textural features for image classification, IEEE Trans. Syst. Man Cybernt., 3, 610-621 (1973), incorporated by reference herein in its entirety for all purposes.
The Sea Surface Temperature Analysis and Composite (SSTAC) module is part of the Navy Tactical Environmental Support System (TESS) created to provide ships at sea with the capability to produce SST imagery from real-time 1-km-resolution AVHRR data. Currently, the module includes time efficient routines to identify and remove clouds from these data. Unfortunately, the daytime version of the cloud-masking technique is highly conservative. It overestimates cloud cover and removes a large number of cloud free pixels from each scene. The nighttime version of the algorithm, although less rigorous than the daytime version, produces noisy data sets and allows for a large number of cloud-contaminated pixels to enter the SST calculations. See Phegley, L. and Crosiar, C., The third phase of TESS, Bull. Am. Meteoroi. Soc., 72, 954-960 (1991), incorporated by reference herein in its entirety for all purposes. None of the techniques reviewed met the accuracy, time efficiency and geographical independence required for the production of accurate SST images.
Current methods of cloud identification and removal in satellite imagery of the oceans rely on spectral features of the scene exclusively. In some cases the methods are overly conservative and tend to remove large numbers of cloud free pixels. In other cases, the methods perform satisfactorily for very cold clouds with high albedo values but underestimate the low-level clouds whose temperature and albedos are similar to that of the ocean. Cirrus, low stratus and small cumulus are examples of such clouds. In addition, the current methods of cloud removal do not perform consistently over a wide range of latitudes and environmental conditions. In fact, they do not perform consistently within the same image. Accurate retrievals of oceanographic parameters such as the SST and ocean color depend largely on an accurate method of cloud removal. For a method of cloud detection to be useful it needs to be: (1) automated, (2) time efficient, (3) accurate, and (4) independent of geographic location and environmental conditions. Current methods of cloud detection do not meet these requirements.